Combined Controllers that Follow Imperfect Input Motions for Humanoid Robots

Humanoid robots have the potential to become a part of everyday life as their hardware and software challenges are being solved. In this paper we present a system that gets as input a motion trajectory in the form of motion capture data, and produces a controller that controls a humanoid robot in real-time to achieve a motion trajectory that is similar to the input motion data. The controller expects the input motion data not to be dynamically feasible for the robot and employs a combined controller with corrective components to keep the robot balanced while following the motion. Since the system can run in real-time, it can be thought of a candidate for teleoperation of humanoid robots using motion capture hardware

Automated Motion Synthesis for Virtual Choreography

In this paper, we present a technique to automati-cally synthesize dancing moves for arbitrary songs. Our current implementation is for virtual characters, but it is easy to use the same algorithms for entertainer robots, such as robotic dancers, which fits very well to this year’s conference theme. Our technique is based on analyzing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the character’s movement synchronizes to the musical beats. In order to analyze beats of the tune, we developed a fast and novel algorithm. Our motion synthesis algorithm analyze library of stock motions and generates new sequences of movements that were not described in the library. We present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music

Better Group Behaviors in Complex Environments using Global Roadmaps

While many methods to simulate flocking behaviors have been proposed, these techniques usually only provide simplistic navigation and planning capabilities because each flock member's behavior depends only on its local environment. In this work, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors and supports global navigation and planning

Enhancing Randomized Motion Planners: Exploring with Haptic Hints

In this paper, we investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our work is motivated by our experience that automatic motion planners sometimes fail due to the difficulty of discovering `critical' configurations of the robot that are often naturally apparent to a human observer. Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine `natural' ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, usergenerated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful represen..